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Jelena Zdravkovic is a Professor and Head of the Department of Computer and Systems Sciences (DSV) at Stockholm University. She leads the PRECIS research group which focuses on Process, Requirements, Enterprise, Capability, and Information Systems modelling. Her work spans theoretical and practical aspects of enterprise and IT solutions with a particular emphasis on digital transformation. Professor Zdravkovic's research interests center around Digital Business Ecosystems , Digital Twins , and Data-driven Requirements Engineering . Her work in Enterprise Modeling explores capability-oriented and consumer-oriented approaches to requirements engineering. She investigates how digital transformation and big data can be leveraged to improve requirements elicitation processes, and how organizations can model and manage complex digital business ecosystems. Her research has significant implications for how businesses can adapt to rapidly changing technological environments while maintaining resilience and competitiveness. Her recent publications reveal a clear trajectory toward integrating artificial intelligence with digital modeling techniques, particularly in the context of smart buildings and business ecosystems. There's a consistent focus on how data-driven approaches can transform traditional requirements engineering practices, making them more responsive to the velocity and variety of digital data sources. Her work bridges theoretical modeling with practical applications across various industries including healthcare, energy, and transportation. Professor Zdravkovic has been actively involved in mentoring PhD students, including supervising research on the Management Framework of Resilient Digital Business Ecosystems. She has participated in numerous national and international projects focused on interoperability and model-driven engineering, securing research funding for innovative work at the intersection of business and technology. She leads the PRECIS research group which deals with theories, methods and tools for analysis and design of organizational and IT solutions in congruence. The group's research covers three key topics – Enterprise Modelling, Business Process Management, and Conceptual Modelling. Their work brings together academic rigor with practical applications to solve real-world business challenges through innovative information systems approaches.

• Requirements Engineering
• Enterprise Modeling
• Business Process Management
• +6 more

Dilian Gurov is a Professor in Computer Science at KTH Royal Institute of Technology, associated with the Digital Futures Faculty and the Division of Theoretical Computer Science. He also coordinates the Doctoral Programme in Computer Science at the CSC school. Before joining KTH in 2002, he earned a Ph.D. from the University of Victoria, Canada (1998), and worked at the Swedish Institute of Computer Science (1997-2002). His research focuses on software specification and verification, including contracts, program models, logics, and tools, as well as multi-agent strategic planning involving knowledge-based strategies in imperfect information settings. Key contributions include the CAV Distinguished Paper Award 2023 for 'Automatic Program Instrumentation for Automatic Verification' and an EASST award for 'Checking Absence of Illicit Applet Interactions: A Case Study' (2004). He leads projects funded by VR (SEFROS, ContraST) and Vinnova (AVerT2) and collaborates with industries like Scania on formal verification of C programs. His service roles span over 30 conference committees and organization roles, including PC memberships for iFM, TAP, and ISoLA. Teaching responsibilities include courses such as 'Formal Methods,' 'Program Semantics and Analysis,' and 'Knowledge in Games with Imperfect Information.' His work emphasizes practical applications of formal methods, bridging academic research with industry needs through collaborations and tool development (e.g., CVPP, ProMoVer, TriCo).

• Software Specification and Verification
• Multi-Agent Strategic Planning
• Formal Methods
• +3 more

Joakim Lindblad is a Professor at the Department of Information Technology, Uppsala University , and holds affiliated roles as Senior Research Associate at the Mathematical Institute of the Serbian Academy of Sciences and Arts, and Head of Research at Topgolf Sweden AB. With over two decades of expertise in image analysis and machine learning , his work bridges computational methods with biomedical applications. Key affiliations: Uppsala University, Serbian Academy of Sciences, Topgolf Sweden Specializations: Deep Learning, Multimodal Image Registration, Quantitative Microscopy His research focuses on reliable image processing frameworks that integrate intensity and spatial information , particularly for biomedical applications . Recent publications highlight innovations in autofluorescence-based cancer detection , self-supervised one-class learning for sparse instance identification, and rotation-equivariant CNNs for robust analysis of cytology images. Recent article trends demonstrate expertise in multimodal image analysis (2024: 3 papers), oral cancer detection (2025: 2 papers), and multiscale biomedical imaging . His 2025 work on the Uppsala Storytelling Dataset introduces novel frameworks for multimodal dataset creation in AI research. While no scientific awards are explicitly mentioned, his extensive publication record (2000-2025) across top venues like Pattern Recognition , PLOS ONE , and IEEE Transactions indicates significant academic impact. His methodological contributions span stochastic distance transforms , fuzzy set defuzzification , and multimodal image registration techniques. Collaborative work with researchers like Nataša Sladoje and interdisciplinary teams has produced innovations in automated cytology analysis , TEM image enhancement , and AI-driven medical diagnostics . His 2021-2022 projects introduced contrastive learning approaches for multimodal image registration and explainable AI frameworks for infant engagement analysis.

• Image Analysis
• Artificial Intelligence
• Deep Learning
• +5 more

Heiko Gebauer is an Adjunct Professor at Linköping University's Department of Management and Engineering (IEI),隶属于 Business Administration (FEK) School. His research focuses on business innovation in technology-driven contexts, including IoT business management, data-driven strategies, and product-service systems. He has published extensively on topics such as IoT key performance indicators, service-centric business model transformations, and data privacy in product-based industries. Recent publications highlight his exploration of healthcare manufacturing innovation, electric vehicle business models, and challenges in IoT platform development. His work addresses strategic management challenges in digital transformation and technology adoption. While no formal advisees or grants are listed, his research collaborations span academic and industry partnerships, as evidenced by co-authored papers with institutions like ETH Zurich and TU Dresden. He contributes to advancing strategic frameworks for product companies navigating IoT and servitization trends.

• Business Innovation
• Technology Management
• Digital Transformation
• +3 more

Karl Palmskog is a Lecturer at KTH Royal Institute of Technology in the Division of Theoretical Computer Science and the STEP research group. His work focuses on program verification and proof engineering, with particular emphasis on developing techniques and tools based on proof assistants for constructing functionally correct and secure software systems. Palmskog received his Ph.D. in Computer Science in 2014 from KTH, advised by Mads Dam, and his M.Sc. in Computer Science and Engineering from KTH in 2007. Prior to his current position, he was a postdoc at The University of Texas at Austin and University of Illinois at Urbana-Champaign. His research interests span programming languages, software engineering, and formal verification, with a particular focus on developing techniques and tools based on proof assistants. He is an avid user of the Coq proof assistant for both proving and programming, often complemented by OCaml, and also utilizes HOL4 and other ML family dialects. His work bridges theoretical foundations with practical applications, particularly in the domains of blockchain systems, distributed systems, and automotive software verification. Analysis of his recent publications reveals a strong focus on Coq-based verification, with significant contributions to proof engineering tools and methodologies. His work includes developing tools for regression proving, change impact analysis, mutation testing for Coq projects, and lemma name suggestion using deep learning. There's also a growing trend toward applying formal methods to real-world systems like blockchain protocols and automotive software. Palmskog has been involved in several research projects, including Coq-community Proof Engineering and Distributed Components. His past projects include Trustfull (SSF), Model-based Event Driven Scalable Programming for the Mobile Cloud (NSF), Highly Adaptable and Trustworthy Software (EU FP7), and 4WARD Future Internet (EU FP7). As an educator, Palmskog has served as examiner, course responsible, teacher, and assistant for various courses including Algorithms, Data Structures and Complexity; Degree Projects; Game Theory; Parallel and Distributed Computing; and Programming Paradigms. His work on Chip, a Coq formalization of change impact analysis, demonstrates his commitment to creating practical, certified tools that bridge formal methods with software engineering practice.

• Program Verification
• Proof Engineering
• Formal Methods
• +4 more

Professor Anne-Kathrin Peters is an Associate Professor in Technology Education at KTH Royal Institute of Technology's School of Industrial Engineering and Management (ITM), affiliated with the Department of Learning. She serves as Co-PI for the Digital Futures project addressing cognitive disabilities in education and co-leads the TREES research cluster. Her research focuses on sustainability in education, equity, and the intersection of social/environmental sustainability. Key roles include coordinating KTH's education for sustainable development initiatives and advising Norway's Excited Centre on IT education. She supervises two PhD students and teaches courses like 'Teaching and Learning in Higher Education.' Research Interests: Education for sustainable development Social and environmental sustainability intersections Norms/identities in computing education Emotions and care ethics in teaching Inclusive digitalization for cognitive disabilities Projects: Digitalisation and Educational Transformation project (Digital Futures) Anthropocene natural science education initiative Collegial networks on equality/sustainability in education at KTH Teaching: Leads core courses for educators, including pedagogical frameworks, gender theory, and challenge-driven education.

• Education Technology
• Sustainability Education
• Computing Education
• +8 more

Elena Troubitsyna is a Professor of Computer Science with specialization in Software Engineering at KTH Royal Institute of Technology. Her research focuses on developing dependable, autonomous systems that ensure safety and reliability, particularly in complex environments like self-driving cars and drones. She employs rigorous mathematical modeling and verification techniques to address system complexity and real-time adaptability challenges. Her work emphasizes the co-engineering of safety and security in cyber-physical systems, integrating formal methods such as Event-B modeling with AI-driven solutions. Key research areas include cybersecurity for embedded systems, formal analysis of safety-security interactions, and resilient multi-agent systems. Elena has contributed to advancing methods for autonomous system navigation, fault tolerance, and privacy-preserving microservices architectures. Elena has organized international workshops like SENSEI (Safety-Security Interaction) and published extensively on topics such as model-driven engineering, formal verification of critical systems, and optimizing scheduling for distributed computing. Her research bridges theoretical foundations with practical applications, aiming to enhance societal trust in autonomous technologies.

• Autonomous Systems
• Software Engineering
• Formal Verification
• +3 more

Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.

• artificial intelligence
• image analysis
• deep learning
• +8 more

Anders Forslund is a researcher at Chalmers University of Technology, specializing in Product Development with a focus on aerospace and mechanical engineering. His work bridges theoretical research and practical application in multidisciplinary design. Department: Product Development Key Collaborations: VINNOVA, EU Horizon 2020, AIAA, ASME His research interests revolve around robust design and uncertainty modeling for aerospace structures. He develops simulation platforms to minimize geometric variation in welded components and optimize turbine lifecycle robustness. His work integrates genetic algorithms , 3D scanning , and PLM systems for multidisciplinary convergence. The 15 most recent publications span 2011–2018, covering welding optimization , geometric robustness , simulation frameworks , and sustainable aerospace design . Key trends include bridging CAD/point cloud gaps , virtual trimming , and set-based design in collaborative platforms. Anders participates in grants and projects such as: TOICA (2013–2016): EU-funded thermal-driven aircraft design. ELISE (2018–2018): Vinnova-funded electric aviation. Design av de aerodynamiska egenskaperna för ett elektriskt flygplan (2021–2022): Chalmers-led aerodynamic R&D. He has contributed to research teams in propulsion, thermal sciences, and acoustic engineering, collaborating with experts like Rikard Söderberg and Lars Davidson.

• Aerospace Engineering
• Mechanical Engineering
• Robust Design
• +4 more

Petter Falkman is an Associate Professor in Systems and Control Engineering at Chalmers University of Technology, where he leads research in the Automation research group. With 68 publications spanning over two decades, his work bridges theoretical control systems with practical industrial applications, particularly in manufacturing and robotics. His research has been supported by major funding bodies including VINNOVA and European Commission projects. Falkman's research primarily focuses on intelligent automation systems, with key contributions in sequence planning, virtual commissioning, and human-robot interaction. His work integrates formal methods with practical industrial applications, developing frameworks like the Sequence Planner for control of intelligent automation systems. His recent publications demonstrate a strong emphasis on data-driven approaches, digital twin technologies, and the application of virtual reality for industrial applications. The research shows a clear trajectory toward increasingly sophisticated integration of human factors with automation systems, particularly through eye tracking and movement prediction technologies. Falkman has led or participated in 10 major research projects from 2011-2025, including CLOUDS (2022-2025) on circular solutions for sustainable production systems, UNICORN (2017-2021) on robotic refuse handling, and several VINNOVA-funded projects on virtual preparation and industrial automation. His collaborative network includes researchers across Chalmers and industry partners like Volvo Group, demonstrating strong industry-academia connections. Falkman has established himself as a key contributor to the development of frameworks for intelligent automation, with particular expertise in translating theoretical control concepts into practical industrial applications. His work on the Sequence Planner framework represents a significant contribution to the field, enabling more efficient preparation and commissioning of automation systems.

• Automation Systems
• Control Engineering
• Robotics
• +5 more

Huadong Yao is an Assistant Professor at the Department of Marine Engineering, Chalmers University of Technology. His research spans renewable energy , fluid-structure interaction (FSI) , and transportation systems , with a focus on offshore wind farms, wave energy, and aero/hydroacoustics. Multi-University Collaboration : Guest professorships at international institutions Leadership Roles : Coordinator of Horizon 2020 projects (e.g., IVANHOE) and guest editor for journals Key Organizations : Member of AIAA, SAE International, RINA, ICNMT, and ICES Working Group on Offshore Renewable Energy His research integrates CFD and FSI coding (OpenFOAM, in-house codes) with turbulence modeling (LES, SNGR) to address problems in marine hydrodynamics (e.g., rim-driven thrusters, wind-powered ship propulsion) and terrestrial transportation (high-speed train aerodynamics, urban air mobility). Recent work explores biomechanics (whiplash injury hydrodynamics) and battery cooling systems for electric vehicles. Current projects focus on: Optimization of wave energy converter farms (hexagon layouts, mooring fatigue) Hubless rim-driven thruster design (gap geometry, concave cavities) Hydrographic impacts of offshore wind turbines on marine environments Urban air mobility (UAM) aerodynamics Multidisciplinary Design Optimization (MDO) using machine learning He collaborates with institutions like AIAA, SAE, and ICES, and has received funding from Horizon 2020 and Swedish national agencies.

• Renewable Energy
• Fluid-Structure Interaction (FSI)
• Aeroacoustics
• +5 more

Pär Strand is a Professor at Chalmers University of Technology, affiliated with the Department of Astronomy and Plasma Physics. His research focuses on transport in fusion plasmas , particularly through analysis of experiments at JET and development of simulation tools for ITER and other tokamak facilities. A key contributor to EU projects, he directs the Chalmers e-Science Centre, emphasizing data-driven methodologies and large-scale simulation technologies. Expertise: Fusion plasma dynamics, electromagnetic field theory, integrated modeling frameworks Projects: Code development for ITER/JET, FAIR data principles in fusion research, turbulence transport simulations Research Trends: Recent publications highlight advancements in: Tokamak power exhaust solutions (divertor shaping, neutral baffling) Machine learning applications for pedestal dynamics and disruption prediction High-order solvers for plasma transport equations Validation of D-T fusion power predictions against JET experiments

• Fusion Plasmas
• Tokamak Experiments
• Electromagnetic Field Theory
• +4 more

Alejandro Russo is a Professor at Chalmers University of Technology , specializing in Information Flow Control (IFC) , Secure Programming Languages , and Functional Programming . His research bridges theoretical foundations and practical implementations, focusing on mitigating timing channels , covert channels , and data leakage in concurrent systems. Developed novel frameworks for Differential Privacy with provable accuracy bounds Pioneered COWL integration for browser security and instruction-based scheduling to prevent cache timing attacks Led major projects like HIPSTER (hybrid static/dynamic IFC) and AppFlow (practical IFC deployment) His publications reveal expertise in security libraries for Haskell and Python , with a focus on faceted execution , label manipulation , and mechanized security proofs . Students under his supervision have explored topics ranging from secure eDSLs to privacy-preserving compilation techniques . Scientific awards : Google Research Award (2011) for Python taint analysis Advising and grants : Principal Investigator for VR , STINT , and Google Research Award Supervised 12+ PhD and Master’s students in security and functional programming research

• Computer Science
• Information Security
• Functional Programming
• +3 more

Marlene Ågerstrand is a Senior Lecturer at the Department of Environmental Science , Stockholm University . Her research focuses on the science-policy interface in chemical risk assessment and management, emphasizing how scientific data inform decision-making, the effectiveness of regulatory strategies, and the role of experts in policy development. She actively collaborates with ecologists, toxicologists, chemists, political scientists, and environmental lawyers. Key affiliations: International Panel on Chemical Pollution (IPCP) Board member Co-developer of the SciRAP web tool for structured chemical evaluations Member of the Baltic Sea Fellows interdisciplinary network Research Interests include improving environmental risk assessments of chemicals, science-policy interactions in chemical management, and pathways to a non-toxic circular economy. She specializes in qualitative methods like document analysis and stakeholder interviews. Recent Publications span environmental quality standards under the Water Framework Directive, essential-use concepts in REACH authorization, regulatory disparities in antimicrobial substance controls, and systematic review frameworks for endocrine disruptors.

• Chemical Risk Assessment
• Science-Policy Interface
• Environmental Toxicology
• +4 more

Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University and a Senior Performance Engineer. She holds a PhD in Computer Science from Simula Research Lab and Universitetet i Oslo (2017), focusing on robustness in multipath transport protocols like MPTCP. Her research spans network performance, security, and congestion control in mobile/5G networks and the Internet. She collaborates actively with academia and industry, co-supervising students in areas such as edge computing, container orchestration, and distributed systems. Affiliations: Department of Informatics, Karlstad University; Red Hat Research; Ericsson R&D. Education: PhD (2017), Simula/UiO; Master’s and Undergraduate studies emphasized networking and electronics. Her work includes projects like AIDA (AI-driven edge networking) and DRIVE (latency-sensitive mobile services). She has published over 50 papers on topics like QUIC, eBPF, and containerized microservices. Awards include the Best Paper at IEEE ICIN 2021 and ANRP 2025 Prize. Teaching responsibilities include Future Internet Design and Service Quality . Advising spans 15+ students across institutions like TU Berlin, KTH, and Unifesp. She chairs conferences (e.g., ACM SIGCOMM 2025) and serves on editorial boards (IEEE Communications Magazine). Key interests: network observability, low-latency protocols, and sustainability in networking.

• Network Performance
• Cybersecurity
• Congestion Control
• +4 more